- python 3.6
- pytorch >= 0.4.1
- torchvision>=0.2.1
- opencv-python>=3.4.2.17
- numpy
- tensorflow>=1.13.0
- visdom
Hyojin Park, Youngjoon Yoo, Geonseok Seo, Dongyoon Han, Sangdoo Yun, Nojun Kwak " C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation " (paper)
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo, Nicolas Monet, Jihwan Bang, Nojun Kwak " SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder" (paper)
| Model | # of Param(M) | # of Flop(G) | size for Flop | IoU( val ) | IoU (test) | server link |
|---|---|---|---|---|---|---|
| C3Net[2,3,7,13] | 0.19 | 3.15 | 512*1024 | 66.87 | 64.78 | link |
| C3NetV2[2,4,8,16] | 0.18 | 2.66 | 512*1024 | 66.28 | 65.48 | link |
| SINet | 0.12 | 1.22 | 1024*2048 | 68.22 | 66.46 | link |
- C3NetV2 has same encoder structure with C3Net, but uses bilinear upsampling for a decodder structure.
- SINet is accepted in WACV2020.
Once you train the model, my code automatically export format for Cityscape Testserver from best training model. I used P-40 GPU for training. C3 and C3_V2 require 2 GPU and SINet needs 1 GPU. Train validation txt is for datalodaer function here
python main_multiscale.py -c C3.json
python main_multiscale.py -c C3_V2.json
python main_Auxloss.py -c SINet.jsonIf our works is useful to you, please add two papers.
@article{park2018concentrated,
title={Concentrated-Comprehensive Convolutions for lightweight semantic segmentation},
author={Park, Hyojin and Yoo, Youngjoon and Seo, Geonseok and Han, Dongyoon and Yun, Sangdoo and Kwak, Nojun},
journal={arXiv preprint arXiv:1812.04920},
year={2018}
}
@article{park2019sinet,
title={SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder},
author={Park, Hyojin and Sj{\"o}sund, Lars Lowe and Monet, Nicolas and Yoo, YoungJoon and Kwak, Nojun},
journal={arXiv preprint arXiv:1911.09099},
year={2019}
}
We are grateful to Clova AI, NAVER with valuable discussions.
I also appreciate my co-authors YoungJoon Yoo, Dongyoon Han, Sangdoo Yun and Lars Lowe Sjösund from Clova AI, NAVER, Nicolas Monet from NAVER LABS Europe and Jihwan Bang from Search Solutions, Inc
I refer ESPNet code for constructing my experiments and also appreciate Sachin Mehta for valuable comments. Sachin Mehta is ESPNet and ESPNetV2 author.
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